Predicting the Masses of Exotic Hadrons with Data Augmentation Using Multilayer Perceptron

TL;DR

This paper demonstrates that data augmentation enhances neural network predictions of exotic hadron masses, achieving results comparable to established models and extending predictive capabilities to less-studied baryons.

Contribution

The study introduces data augmentation techniques to neural networks for predicting exotic hadron masses, improving accuracy and expanding applicability in particle physics.

Findings

Data augmentation improves neural network prediction accuracy.

Neural networks can predict masses of exotic hadrons effectively.

Predictions are comparable to Gaussian Process and Constituent Quark Model.

Abstract

Recently, there have been significant developments in neural networks, which led to the frequent use of neural networks in the physics literature. This work is focused on predicting the masses of exotic hadrons, doubly charmed and bottomed baryons using neural networks trained on meson and baryon masses that are determined by experiments. The original data set has been extended using the recently proposed artificial data augmentation methods. We have observed that the neural network's predictive ability increases with the use of augmented data. The results indicated that data augmentation techniques play an essential role in improving neural network predictions; moreover, neural networks can make reasonable predictions for exotic hadrons, doubly charmed, and doubly bottomed baryons. The results are also comparable to Gaussian Process and Constituent Quark Model.